The goal of this proposal is to develop an integrated probabilistic approach to functional brain imaging using electromagnetic and hemodynamic techniques that capitalizes upon the strengths and minimizes the weaknesses of each technique alone. The fundamental rationale for attempting to integrate electromagnetic and hemodynamic imaging techniques is: (1) no single technique provides the spatial and temporal resolution needed for clinical and research applications; and (2) hemodynamic and electromagnetic techniques have complementary strengths and weaknesses that can be exploited in an integrated analysis. The mathematical basis for our probabalistic approach is Bayesian Inference. In contrast to most existing approaches to analysis of functional imaging data the results of our Bayesian inferential approach is not a single "best" estimate of brain activity according to some criterion, but rather estimates of the full probability distribution for parameters of interest. Furthermore, Bayesian inference can incorporate uncertain models, such as those of the hemodynamic response in fMRI or of the EEG forward model, which depends on uncertain conductivity profiles. We believe this Bayesian inference approach could significantly improve our ability to gain robust spatial-temporal information on neural activation from existing functional neural imaging modalities. In order to realize this we propose to, 1) develop a fully integrated analysis of fMRI and MEG data, 2) develop a probabilistic EEG foward model so that our MEG analysis can be used for EEG data, and 3) distribute, optimize, test and refine the spatial-temporal MEG analysis.